Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type
View/
salminen_et_al_2019.pdf (584.9Kb)
Download
Publication date
2019-08Rights
© 2019 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-Reviewed
YesMetadata
Show full item recordAbstract
As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.Version
Accepted manuscriptCitation
Salminen J, Yoganathan V, Corporan J et al (2019) Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research. 101: 203-217.Link to publisher’s version
https://doi.org/10.1016/j.jbusres.2019.04.018Type
ArticleCollections
Related items
Showing items related by title, author, creator and subject.
-
Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on TwitterMarketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.
-
Semantic content analysis for effective video segmentation, summarisation and retrieval.This thesis focuses on four main research themes namely shot boundary detection, fast frame alignment, activity-driven video summarisation, and highlights based video annotation and retrieval. A number of novel algorithms have been proposed to address these issues, which can be highlighted as follows. Firstly, accurate and robust shot boundary detection is achieved through modelling of cuts into sub-categories and appearance based modelling of several gradual transitions, along with some novel features extracted from compressed video. Secondly, fast and robust frame alignment is achieved via the proposed subspace phase correlation (SPC) and an improved sub-pixel strategy. The SPC is proved to be insensitive to zero-mean-noise, and its gradient-based extension is even robust to non-zero-mean noise and can be used to deal with non-overlapped regions for robust image registration. Thirdly, hierarchical modelling of rush videos using formal language techniques is proposed, which can guide the modelling and removal of several kinds of junk frames as well as adaptive clustering of retakes. With an extracted activity level measurement, shot and sub-shot are detected for content-adaptive video summarisation. Fourthly, highlights based video annotation and retrieval is achieved, in which statistical modelling of skin pixel colours, knowledge-based shot detection, and improved determination of camera motion patterns are employed. Within these proposed techniques, one important principle is to integrate various kinds of feature evidence and to incorporate prior knowledge in modelling the given problems. High-level hierarchical representation is extracted from the original linear structure for effective management and content-based retrieval of video data. As most of the work is implemented in the compressed domain, one additional benefit is the achieved high efficiency, which will be useful for many online applications.
-
The Impact of moisture and clay content on the unconfined compressive strength of lime treated highly reactive claysThis study aims to provide a thorough evaluation for the changes in the microstructure and evolution of strength of highly reactive clays that were treated with 7 % lime over a period of curing time as a function of the mixing moisture content. Three series of testing were carried out on specimens with 100 %, 85 % and 75 % of bentonite content and prepared with different moisture content of 10, 20, 30 and 40 % above the corresponding optimum moisture content. Specimens of 100 % bentonite were treated with 7 % of lime, compacted to achieve a predetermined dry unit weight and cured at temperatures of 20 OC and 40 OC for up to 28 days whereas the specimens with 85 % and 75 % of bentonite content were prepared by the addition of sand and were cured at 20 oC for up to 7 days. Unconfined Compressive Strength tests and Scanning Electron Microscopy were conducted to observe the strength and the microstructural changes resulting from increasing mixing moisture content. California Bearing Ratio and Resilient Modulus were correspondingly determined based on correlations with the Unconfined Compressive Strength. The failure pattern was also studied to better understand the ultimate behaviour of lime stabilised clays. The results revealed that the strength of treated bentonite increased with the increase in the moisture content up to 30 % above the corresponding optimum moisture content and with increasing the curing time and temperature. Nevertheless, substituting bentonite with sand on the specimen resulted in a significant reduction on the attained strength. Furthermore, the results of California Bearing Ratio and Resilient Modulus showed that values for both parameters are significantly enhanced with lime treatment. The microstructural analysis provided visual evidence to the improved strength in which the pozzolanic reaction was found to be significantly affected by the amount of moisture in the mixture. The results suggested that compacting lime treated expansive clays with moisture content moderately higher than the optimum moisture content would result in a significant enhancement to the attained strength over the period of curing.
